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Purpose: Diffusion‐weighted steady‐state free precession (DW‐SSFP) is shown to provide a means to probe non‐Gaussian diffusion through manipulation of the flip angle. A framework is presented to define an effective b‐value in DW‐SSFP. Theory: The DW‐SSFP signal is a summation of coherence pathways with different b‐values. The relative contribution of each pathway is dictated by the flip angle. This leads to an apparent diffusion coefficient (ADC) estimate that depends on the flip angle in non‐Gaussian diffusion regimes. By acquiring DW‐SSFP data at multiple flip angles and modeling the variation in ADC for a given form of non‐Gaussianity, the ADC can be estimated at a well‐defined effective b‐value. Methods: A gamma distribution is used to model non‐Gaussian diffusion, embedded in the Buxton signal model for DW‐SSFP. Monte‐Carlo simulations of non‐Gaussian diffusion in DW‐SSFP and diffusion‐weighted spin‐echo sequences are used to verify the proposed framework. Dependence of ADC on flip angle in DW‐SSFP is verified with experimental measurements in a whole, human postmortem brain. Results: Monte‐Carlo simulations reveal excellent agreement between ADCs estimated with diffusion‐weighted spin‐echo and the proposed framework. Experimental ADC estimates vary as a function of flip angle over the corpus callosum of the postmortem brain, estimating the mean and standard deviation of the gamma distribution as urn:x-wiley:0740‐3194:media:mrm28169:mrm28169-math-0018 mm2/s and urn:x-wiley:0740‐3194:media:mrm28169:mrm28169-math-0019 mm2/s. Conclusion: DW‐SSFP can be used to investigate non‐Gaussian diffusion by varying the flip angle. By fitting a model of non‐Gaussian diffusion, the ADC in DW‐SSFP can be estimated at an effective b‐value, comparable to more conventional diffusion sequences.

The developing Human Connectome Project is set to create and make available to the scientific community a 4-dimensional map of functional and structural cerebral connectivity from 20 to 44 weeks post-menstrual age, to allow exploration of the genetic and environmental influences on brain development, and the relation between connectivity and neurocognitive function. A large set of multi-modal MRI data from fetuses and newborn infants is currently being acquired, along with genetic, clinical and developmental information. In this overview, we describe the neonatal diffusion MRI (dMRI) image processing pipeline and the structural connectivity aspect of the project. Neonatal dMRI data poses specific challenges, and standard analysis techniques used for adult data are not directly applicable. We have developed a processing pipeline that deals directly with neonatal-specific issues, such as severe motion and motion-related artefacts, small brain sizes, high brain water content and reduced anisotropy. This pipeline allows automated analysis of in-vivo dMRI data, probes tissue microstructure, reconstructs a number of major white matter tracts, and includes an automated quality control framework that identifies processing issues or inconsistencies. We here describe the pipeline and present an exemplar analysis of data from 140 infants imaged at 38–44 weeks post-menstrual age., Highlights • A comprehensive and automated pipeline to consistently analyse neonatal dMRI data. • Optimised motion and distortions correction to address newborn specific challenges. • The automated QC framework allows to detect issues and to quantify data quality. • Automated white matter segmentation allows to extract tract-specific masks. • Preliminary data analysis of 140 infants imaged at 38–44 weeks post-menstrual age.

Contains fulltext : 215853.pdf (Publisher’s version ) (Open Access) Evolutionary adaptations of the human brain are the basis for our unique abilities such as language. An expansion of the arcuate fasciculus (AF), the dorsal language tract, in the human lineage involving left lateralization is considered canonical, but this hypothesis has not been tested in relation to other architectural adaptations in the human brain. Using diffusion-weighted MRI, we examined AF in the human and macaque and quantified species differences in white matter architecture and surface representations. To compare surface results in the two species, we transformed macaque representations to human space using a landmark-based monkey-to-human cortical expansion model. We found that the human dorsal AF, but not the ventral inferior fronto-occipital fasciculus (IFO), is left-lateralized. In the monkey AF is not lateralized. Moreover, compared to the macaque, human AF is relatively increased with respect to IFO. A comparison of human and transformed macaque surface representations suggests that cortical expansion alone cannot account for the species differences in the surface representation of AF. Our results show that the human AF has undergone critical anatomical modifications in comparison with the macaque AF. More generally, this work demonstrates that studies on the human brain specializations underlying the language connectome can benefit from current methodological advances in comparative neuroanatomy. 9 p.

Supplemental digital content is available in the text., Objectives The aim of this study was to describe the development of morphologic and diffusion tensor imaging sequences of peripheral nerves at 7 T, using carpal tunnel syndrome (CTS) as a model system of focal nerve injury. Materials and Methods Morphologic images were acquired at 7 T using a balanced steady-state free precession sequence. Diffusion tensor imaging was performed using single-shot echo-planar imaging and readout-segmented echo-planar imaging sequences. Different acquisition and postprocessing methods were compared to describe the optimal analysis pipeline. Magnetic resonance imaging parameters including cross-sectional areas, signal intensity, fractional anisotropy (FA), as well as mean, axial, and radial diffusivity were compared between patients with CTS (n = 8) and healthy controls (n = 6) using analyses of covariance corrected for age (significance set at P < 0.05). Pearson correlations with Bonferroni correction were used to determine association of magnetic resonance imaging parameters with clinical measures (significance set at P < 0.01). Results The 7 T acquisitions with high in-plane resolution (0.2 × 0.2mm) afforded detailed morphologic resolution of peripheral nerve fascicles. For diffusion tensor imaging, single-shot echo-planar imaging was more efficient than readout-segmented echo-planar imaging in terms of signal-to-noise ratio per unit scan time. Distortion artifacts were pronounced, but could be corrected during postprocessing. Registration of FA maps to the morphologic images was successful. The developed imaging and analysis pipeline identified lower median nerve FA (pisiform bone, 0.37 [SD 0.10]) and higher radial diffusivity (1.08 [0.20]) in patients with CTS compared with healthy controls (0.53 [0.06] and 0.78 [0.11], respectively, P < 0.047). Fractional anisotropy and radial diffusivity strongly correlated with patients' symptoms (r = −0.866 and 0.866, respectively, P = 0.005). Conclusions Our data demonstrate the feasibility of morphologic and diffusion peripheral nerve imaging at 7 T. Fractional anisotropy and radial diffusivity were found to be correlates of symptom severity.

Fifteen years ago, Passingham and colleagues proposed that brain areas can be described in terms of their unique pattern of input and output connections with the rest of the brain, and that these connections are a crucial determinant of their function. We explore how the advent of neuroimaging of connectivity has allowed us to test and extend this proposal. We show that describing the brain in terms of an abstract connectivity space, as opposed to physical locations of areas, provides a natural and powerful framework for thinking about brain function and its variation across the brains of individuals, populations, and species., Highlights The concept of the connectivity fingerprint was described 15 years ago to suggest a crucial relationship between the connections (functional integration) and function (functional segregation) of an area. The advent of neuroimaging now allows for this concept to be applied on a much larger scale, describing brain organization in terms of a connectivity space. Describing brain organization in this space is closer to its functional organization and can predict differences in functional activation and behaviour. By abstracting away into connectivity space one can compare brains across individuals and species. Comparing differences in the connectivity fingerprints of areas or subareal units in terms of gradients, one can investigate which dimension of connectivity space is relevant for understanding particular aspects of brain organization.

Item does not contain fulltext Intra-axonal axial diffusivity could be interesting biomarker of disease, yet it is often assumed constant across the white matter. Furthermore, when intra-axonal diffusivity is estimated, few models account for fibre orientation dispersion which (when not explicitly modelled) will greatly affect the estimates of axial diffusion. Here we combine the stick model of intra-axonal diffusion with a simple model of fibre dispersion to simultaneously estimate intra-axonal axial diffusivity and fibre dispersion on a voxel-wise basis in high b-value data. Our results demonstrate considerable variability in the intra-axonal axial diffusivity across the white matter. 28th Annual Meeting of the International Society for Magnetic Resonance in Medicine (Paris, France, 8-13 August)

We present a new toolbox and library of standardised tractography protocols devised for the robust automated extraction of white matter tracts both in the human and the macaque brain. Using in vivo data from the Human Connectome Project (HCP) and the UK Biobank and ex vivo data for the macaque brain datasets, we obtain white matter atlases, as well as atlases for tract endpoints on the white-grey matter boundary, for both species. We illustrate that our protocols are robust against data quality, generalisable across two species and reflect the known anatomy. We further demonstrate that they capture inter-subject variability by preserving tract lateralisation in humans and tract similarities stemming from twinship in the HCP cohort. Our results demonstrate that the presented toolbox will be useful for generating imaging-derived features in large cohorts, and in facilitating comparative neuroanatomy studies. The software, tractography protocols, and atlases are publicly released through FSL, allowing users to define their own tractography protocols in a standardised manner, further contributing to open science.

Contains fulltext : 177726.pdf (Publisher’s version ) (Open Access) Diffusion MRI is an exquisitely sensitive probe of tissue microstructure, and is currently the only non-invasive measure of the brain's fibre architecture. As this technique becomes more sophisticated and microstructurally informative, there is increasing value in comparing diffusion MRI with microscopic imaging in the same tissue samples. This study compared estimates of fibre orientation dispersion in white matter derived from diffusion MRI to reference measures of dispersion obtained from polarized light imaging and histology. Three post-mortem brain specimens were scanned with diffusion MRI and analyzed with a two-compartment dispersion model. The specimens were then sectioned for microscopy, including polarized light imaging estimates of fibre orientation and histological quantitative estimates of myelin and astrocytes. Dispersion estimates were correlated on region - and voxel-wise levels in the corpus callosum, the centrum semiovale and the corticospinal tract. The region-wise analysis yielded correlation coefficients of r = 0.79 for the diffusion MRI and histology comparison, while r = 0.60 was reported for the comparison with polarized light imaging. In the corpus callosum, we observed a pattern of higher dispersion at the midline compared to its lateral aspects. This pattern was present in all modalities and the dispersion profiles from microscopy and diffusion MRI were highly correlated. The astrocytes appeared to have minor contribution to dispersion observed with diffusion MRI. These results demonstrate that fibre orientation dispersion estimates from diffusion MRI represents the tissue architecture well. Dispersion models might be improved by more faithfully incorporating an informed mapping based on microscopy data.

Diffusion MRI data can be affected by hardware and subject-related artefacts that can adversely affect downstream analyses. Therefore, automated quality control (QC) is of great importance, especially in large population studies where visual QC is not practical. In this work, we introduce an automated diffusion MRI QC framework for single subject and group studies. The QC is based on a comprehensive, non-parametric approach for movement and distortion correction: FSL EDDY, which allows us to extract a rich set of QC metrics that are both sensitive and specific to different types of artefacts. Two different tools are presented: QUAD (QUality Assessment for DMRI), for single subject QC and SQUAD (Study-wise QUality Assessment for DMRI), which is designed to enable group QC and facilitate cross-studies harmonisation efforts., Highlights • Two tools to automatically perform QC of diffusion MRI data. • Automated generation of single subject reports for visual inspection and database. • Group databases and reports allow to compare subjects within and between studies. • Categorical and continuous variables can be used to update the reports.

Phrenology was a nineteenth century endeavour to link personality traits with scalp morphology, which has been both influential and fiercely criticised, not least because of the assumption that scalp morphology can be informative of underlying brain function. Here we test the idea empirically rather than dismissing it out of hand. Whereas nineteenth century phrenologists had access to coarse measurement tools (digital technology referring then to fingers), we were able to re-examine phrenology using 21st century methods and thousands of subjects drawn from the largest neuroimaging study to date. High-quality structural MRI was used to quantify local scalp curvature. The resulting curvature statistics were compared against lifestyle measures acquired from the same cohort of subjects, being careful to match a subset of lifestyle measures to phrenological ideas of brain organisation, in an effort to evoke the character of Victorian times. The results represent the most rigorous evaluation of phrenological claims to date.